Machine to machine (M2M) network communications involves technologies to communicate with other devices often of similar abilities, different from traditional cellular communication networks for instance. In basic M2M environments, a device having limited logic (such as a sensor, meter, etc.) is resident at a location to typically captured measurable event data (such as temperature, pressure, quantity, etc.). The device is connected through a communications network to a remote computer or server having an application layer of specific software. The data received from the device is converted to relevant information associated with the measured event data through the application and may often thereafter undergo analysis or further similar assessment. In many cases a device, when activated, may trigger and communicate the events it is intended for so that those communicated events will then be acted upon by other machines, applications, and/or users on the network.
M2M environments often involve systems of networks, wired and wireless, that are to be connected to the internet and include personal appliances and similar devices. In M2M networks, typically devices may stationary or mobile and be connected via wired or wireless access protocols, often through WiFi network protocols or a 3GPP Mobile network protocol. These devices may also have seasonal and/or elastic connectivity needs (e.g., agricultural business needs, store and forward capability). Often in busy M2M networks, there is an ‘always on’ device being used such as a general packet radio services (GPRS) or internet gateway. However, M2M communication infrastructure remains most suited to the communication needs and patterns of device having similar abilities, characteristically, for communicating with other systems and device on the same network.
FIG. 1 depicts a basic M2M communication network 100 having typical sensor-type devices 120, 130 and 140. In FIG. 1, the M2M network 100 has a central communication gateway 110 in which communications from devices 120, 130 and 140 are linked with a service provider network 150. The linkage may be wired or wireless, and is depicted as the security camera 120 and the water alarm sensor 130 are in wireless communication with the gateway 110. Similarly, the traffic camera sensor 140 is in wired communication with the gateway, though one will appreciate that there are many variations to the type and protocol of communication for FIG. 1.
From FIG. 1, data sensed and obtained by the devices is transmitted across the M2M network to the service provider network 150 where the data may be shared as raw data or converted to information, often though software applications. Notification equipment 160 wirelessly receives the data from the service provider network 150 and acts in accordance with the received data for the specific event. For instance where the notification equipment is an alert system to send a text to a building owner in the event of a water leak, and the water sensor has sent data indicating a water leak, the notification equipment will then trigger an event to notify the building owner. Similarly, from FIG. 1, where the user 170 receives a suite of rolling historical data as to traffic camera operation cycles, the user may then act accordingly based on the received cumulative information.
With the additional device and communication complexities however, M2M networks may often experience communication issues where communications from devices are continually sent to intended recipients in error (e.g., incorrect configurations), communications to non-receiving devices are frequently repeated from the source sender, and the M2M devices may be relentless in their communications as they have limited logic to determine self-awareness and communication benefit. Alarm sensors, for instance, may continuously transmit notifications of an alarm even though the receiving end has already acted upon the triggering event. Switch hoping of stationary devices may occur as well. Often these communication failures create unintended consequences involving bandwidth inefficiencies, traffic disruption, customer outages, further network complexities and additional costs. Unfortunately, many of these issues cannot be derived or determined by log file parsing for instance.
Therefore, what is desired is an approach to intelligently identify patterns, structure and anomalies in diagnostic data of communication events across M2M networks in real time or near real time proactively, and in a scalable and reliable manner.
As used herein the terms device, appliance, terminal, remote device, wireless asset, etc. are intended to be inclusive, interchangeable, and/or synonymous with one another and other similar communication-based equipment for purposes of the present invention though one will recognize that functionally each may have unique characteristics, functions and/or operations which may be specific to its individual capabilities and/or deployment.
As used herein the term M2M communication is understood to include methods of utilizing various connected computing devices, servers, clusters of servers, wired and/or wirelessly, which provide a networked infrastructure to deliver computing, processing and storage capacity as services where a user typically accesses applications through a connected means such as but not limited to a web browser, terminal, mobile application (i.e., app) or similar while the primary software and data are stored on servers or locations apart from the devices.